Form Of eWOM Research Articles

Vive la résistance: a boomerang effect of resisting incentivized eWOM on consumer trust

Incentivizing consumers to share product recommendations and other forms of eWOM (electronic word of mouth) with their social media friends has become a prevalent promotional strategy among marketers. However, could this strategy potentially backfire? Adopting a metacognitive perspective, we suggest that this approach adversely affects those consumers who reject the incentive and resist sharing the message. We hypothesize that for individuals who rejected an incentive to share a message, lower initial message trustworthiness leads to decreased subsequent message trustworthiness. This effect arises as consumers reflect on their own behavior, subsequently developing perceived manipulative intent. This proposed sequential process was substantiated in two experimental studies. The results demonstrate a negative effect of initial eWOM trustworthiness on subsequent trustworthiness following incentive rejection (Studies 1-2). Furthermore, this effect is mediated by perceived manipulative intent and is bounded by the size of the incentive offered for sharing eWOM (Study 2). Both theoretical and practical implications of these findings are discussed.

Research on the influencing factors of public comments emotion on government microblogs

The rapid development of online social media has attracted a large number of users, and government microblogs have widely spread their influence by virtue of the openness of the social media platform itself. In recent years, government microblogs have gradually become important channels for the public to pay attention to government policy information and understand the effect of government policy. The public comments in government microblogs are a form of E-WOM, and the emotions contained in them can reflect the public's recognition of the government's governance effects. The purpose of this study is to explore the influencing factors of public comments emotion on government microblogs.First,we establish a conceptual model of the influencing factors of people's emotions on the basic of the previous literatures.And use LDA topic extraction, sentiment analysis and text classification methods, 20 government affairs microblogs in 5 major cities are collected to verify, and the results show that bloggers use emotional language frames, choose appropriate microblog topics, and actively respond to public comments, which have a positive impact on public commentary sentiment; however, blog posts that contain media items or blog posts that are too short will make People have negative emotions. The research results are helpful to optimize the operation of government microblogs, have a good emotional interaction with the public, and make the public more recognize the government's governance effect.

E-WOM in the B2B context: Conceptual domain, forms, and implications for research

Despite a vast increase in electronic word-of-mouth (e-WOM) research over the past two decades, insights into e-WOM in the Business-to-Business (B2B) context have been limited, resulting in an incomplete understanding of its meaning and the market implications. The existing conceptualizations of e-WOM are deeply entrenched in the characteristics of a particular context—Business-to-Customers (B2C)— and, consequently, focus exclusively on end customers. The diversity of B2B stakeholders and the complexity of their relationships in the online communicative space demand a clearer understanding of the e-WOM concept in this specific context. This paper synthesizes the key studies in this area and proposes an organizing framework for online B2B communicative networks based on a stakeholder approach. It differentiates between seven forms of e-WOM, illustrating its complexity in B2B contexts and allowing a fine-grained understanding of the phenomenon. Informed by the literature reviewed, this paper offers a more nuanced conceptualization of e-WOM, emphasizing its ability to cover a range of contexts. It enables firms to fully understand the concept and manage their relationships with different e-WOM actors/stakeholders in the online environment. A future research agenda is provided for the field, allowing researchers to direct and craft B2B e-WOM theory and practice.

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca.

Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers' behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers' demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers' decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k-means approaches to develop the hybrid method. We collect data from travelers' online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers' satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction.

Intensity, positive valence, negative valence, and content of electronic word of mouth influence online shopping intention

The internet allows consumers to easily provide testimonials about sellers and the products they buy. This testimony is a form of E-WOM with the dimensions of intensity, positive valence, negative valence, and content. By taking Tokopedia as the context, this study is intended to determine the effect of these dimensions on the purchase intention of Tokopedia consumers. The sample consisted of 70 respondents who were selected using non-probability sampling. By using multiple linear regression, data analysis shows that intensity, negative valence, and content have a significant positive effect on repurchase intention, but positive valence has no effect. It seems that consumers are more concerned about the risks of online purchases. Other researchers are advised to investigate this possibility further. Plagiarism Check

The Role of Electronic Worth of Mouth in Mediating TikTok Social Media Toward the Organizational Image of Universitas Muhammadiyah Ponorogo

The use of social media as a new medium in marketing has been considered quite effective. The users obtain plenty of marketing benefits through social media. TikTok social media has been recently famous amidst the society. This social media with a short video has the advantages to draw the attention of the viewers. The use of social media in marketing creates comments in the form of E-WOM which enables it to impact the image of an organization or company. This research uses a quantitative research method, taking 100 members of the campus tour of Universitas Muhammadiyah Ponorogo in 2021 as the sample. The marketing strategy using social media is executed by the technical implementation unit of the new student admissions of Universitas Muhammadiyah Ponorogo by creating a short video uploaded in its Instagram account. The organizational image becomes a dependent variable of this research which will be tested by the use of TikTok as an independent variable and E-WOM as a mediator variable. The result of this research shows that E-WOM has a positive influence in mediating the impact of TikTok use toward the organizational image of Universitas Muhammadiyah Ponorogo.

The Role of Electronic Worth of Mouth in Mediating TikTok Social Media Toward The Organizational Image of Universitas Muhammadiyah Ponorogo

The use of social media as a new medium in marketing has been considered quite effective. The users obtain plenty of marketing benefits through social media. TikTok social media has been recently famous amidst the society. This social media with a short video has the advantages to draw the attention of the viewers. The use of social media in marketing creates comments in the form of E-WOM which enables it to impact the image of an organization or company. This research uses a quantitative research method, taking 100 members of the campus tour of Universitas Muhammadiyah Ponorogo in 2021 as the sample. The marketing strategy using social media is executed by the technical implementation unit of the new student admissions of Universitas Muhammadiyah Ponorogo by creating a short video uploaded in its Instagram account. The organizational image becomes a dependent variable of this research which will be tested by the use of TikTok as an independent variable and E-WOM as a mediator variable. The result of this research shows that E-WOM has a positive influence in mediating the impact of TikTok use toward the organizational image of Universitas Muhammadiyah Ponorogo.

Comparing email and SNS users: Investigating e-servicescape, customer reviews, trust, loyalty and E-WOM

For better or worse, satisfied or dissatisfied customers function as trusted branding faces for organizations as they transmit potentially viral messages through E-WOM. These E-WOM messages can prove marketers' best friends or worst enemies, depending on their tenor. With data from actual customers of an e-tailer, this research enriches our understanding of how e-servicescape is linked to E-WOM. E-servicescape captures the online environmental factors of marketers’ websites. Two forms of E-WOM are examined; i.e., emails and social network postings. Customer reviews were investigated as a prospective sub-dimension of e-servicescape. Findings suggest that customer reviews play an integral role in the e-servicescape construct, that e-servicescape positively impacts trust and that trust positively influences E-WOM and customer loyalty. Two groups of customers were compared, and results indicated differences between email and SNS users. Theoretical and managerial insights related to E-WOM and electronic commerce shopping behavior were generated.

The Retweet as a Function of Electronic Word-of-Mouth Marketing: A Study of Athlete Endorsement Activity on Twitter

The purpose of this study was to examine endorsement-related tweets from athletes and determine which characteristics of those tweets could increase the degree of electronic word-of-mouth marketing (eWOM) generated by the message. Previous literature has suggested that the retweet function in Twitter is a form of eWOM. Through the lens of eWOM, the concepts of vividness, interactivity, and congruence are used to understand what tweet characteristics generate the most retweets. A sample of professional-athlete endorsement and sponsored tweets (n = 669) was used and coded based on frameworks adapted from previous studies. Results indicated that the interaction between levels of high vividness and high interactivity generated the highest frequency of retweets. Reported findings could inform athletes and/or brand managers in ways to increase the eWOM of sponsored messages on Twitter.

Using Twitter to engage with customers: a data mining approach

Purpose – The purpose of this paper is to explore customer engagement in Twitter via data mining. Design/methodology/approach – This study’s intended contributions are twofold: to find a clear connection among customer engagement, presumption, and Web 2.0 in a context of service-dominant (S-D) logic; and to identify social networks created by prosumers. To this end, the study employed data mining techniques. Tweets about IKEA were used as a sample. The resulting algorithm based on 300 tweets was applied to 4,000 tweets to identify the patterns of electronic word-of-mouth (eWOM). Findings – Social networks created in IKEA’s tweets consist of three forms of eWOM: objective statements, subjective statements, and knowledge sharing. Most objective statements are disseminated from satisfied or neutral customers, while subjective statements are disseminated from dissatisfied or neutral customers. Satisfied customers mainly carry out knowledge sharing, which seems to reflect presumption behavior. Research limitations/implications – This study provides partial evidence of customer engagement and presumption in IKEA’s tweets. The results indicate that there are three forms of eWOM in the networks: objective statements, subjective statements, and knowledge sharing. It seems that IKEA successfully engaged customers in knowledge sharing, while negative opinions were mainly disseminated in a limited circle. Practical implications – Firms should make more of an effort to identify prosumers via data mining, since these networks are hidden behind “self-proclaimed” followers. Prosumers differ from opinion leaders, since they actively participate in product development. Thus, firms should seek prosumers in order to more closely fit their products to consumer needs. As a practical strategy, firms could employ celebrities for promotional purposes and use them as a platform to convert their followers to prosumers. In addition, firms are encouraged to make public how they resolve problematic customer complaints so that customers can feel they are a part of firms’ service development. Originality/value – Theoretically, the study makes unique contributions by offering a synergic framework of S-D logic and Web 2.0. The conceptual framework collectively relates customer engagement, presumption, and Web 2.0 to social networks. In addition, the idea of examining social networks based on different forms of eWOM has seldom been touched in the literature. Methodologically, the study employed seven algorithms to choose the most robust model, which was later applied to 4,000 tweets.