Examining the effect of eWOM on the customer purchase intention through value co-creation (VCC) in social networking sites (SNSs)

The Nexuses Between Social Media Marketing Activities and Consumers' Engagement Behaviour: A Two-Wave Time-Lagged Study

This study examined how social media marketing activities (SMMA) influence consumers' engagement behaviour in developing countries. Based on the stimulus-organism-response theory, we examined the effect of SMMA on consumers' engagement intention and further investigated the moderating effect of social media sales intensity. The study employed a time-lagged design with two waves to confirm the hypothesised framework. The study findings showed that SMMA positively influence consumers' engagement intention and engagement behaviour. In addition, social media sales intensity strengthens the link between engagement intention and engagement behaviour. This study adds to the literature on social media and discusses its practical implications.

Driving Consumer Value Co-creation and Purchase Intention by Social Media Advertising Value

Social media advertisement (ad) is a growing phenomenon designed to reach and engage customers. However, despite their continued adoption, less remains known regarding the effectiveness of social media ads to co-create brand value. In response to this gap, this study aims to deepen the theoretical understanding of consumer value co-creation through social media advertising value. The data were collected using purposive sampling from 286 experienced social-media users, and the model was tested using partial least square (PLS)-based structural equation modeling. The results indicate that entertainment, aesthetic appeal, interactivity, and trendiness significantly affect the adverting value of social media ads. In turn, ad value affects consumers' intention for value co-creation. Consequently, our findings suggest the importance of social media advertising value where marketers may enhance consumer-brand engagement (CBE) by incorporating interesting content, which may encourage the customer's interaction with the social media ads and strengthen value co-creation behavior. The results further contribute to nascent marketing literature by demonstrating that value co-creation acts as an antecedent to generating positive electronic word-of-mouth (e-WOM) on social media platforms to drive consumers' online brand purchase intention.

The Influence of Electronic Word of Mouth (eWOM) Communications on Citizens' Adoption of Mobile Government Services

Electronic word-of-mouth (eWOM) communications is considered an important force determining the purchasing intentions of consumers in the context of e-commerce and marketing fields but few studies have examined this important element of eWOM communications in the context of mobile government. Therefore, this study is aimed at filling this gap by integrating eWOM communications into the Technology Acceptance Model (TAM) to explore its direct impact on the adoption of mobile government services among Chinese citizens (college students). The analysis was conducted by the use of SPSS through regression analysis. The results showed that all the research hypotheses were statistically supported. Specifically, eWOM mobile government communication was found to significantly predict the perceived usefulness, perceived ease of use, and the behavioral intention to adopt mobile government services. The implications of these findings on the development of mobile government services are thoroughly discussed.

Mining brand value propositions on Twitter: exploring the link between marketer-generated content and eWOM outcomes

Content marketing has become a mainstream channel for brands to engage the market with value propositions. Through content, brands control, instigate and co-create value with its target audience. However, the nature of value propositions embedded within the cycle of content co-creation and their impact on eWOM outcomes has received scant attention from an empirical perspective. In this paper, we propose a value proposition-based framework around content marketing using established business perspectives. We employ bidirectional Twitter data from brands and customers to unearth descriptive, diagnostic and predictive insights into value propositions. Using a sample of marketer- and user-generated data from 10 Coffee (n(MGC) = 290, n(UGC) = 8811) and Car brands (n(MGC) = 635, n(UGC) = 7035) in 2018, a taxonomy of value propositions based on the literature was proposed and validated. The results of our study identify (a) descriptive insights explaining differentiation of brand value propositions, (b) diagnostic insights relating to consumer sentiments in response to the value proposition mix and (c) predictive insights of models predicting brand-specific values’ influencing Like, Share, Comment and Positive/Negative valence. Our results show that an effective social media marketing strategy selectively uses elements of the marketing mix (i.e. 4 P’s) within value propositions to attract favourable eWOM outcomes.

Can Narrative Advertisement and eWOM Influence Generation Z Purchase Intentions?

Digital platforms have made Generation Z able to listen to the story from both sides, i.e., the brand’s promotional messages, and the consumers’ experiences. To capture an audience’s attention on endless entertainment and informational platforms, narrative advertisement is deployed to trigger emotions and feelings. As digital communities continue to grow, both brands and consumers are using narrative advertising to share their opinions. Hence, the purpose of this research was to investigate how the upcoming generation of consumers will be affected by the two information sources, i.e., the brands via narrative advertising, and peer consumers through electronic word-of-mouth (eWOM). Furthermore, the mediated role of persuasion knowledge was examined among the constructs. Theoretical foundations were empirically tested using quantitative research analysis. The data was collected from a sample of 304 Generation Z respondents from Pakistan. Structural equation modeling (SEM) using AMOS 22.0 was executed to determine cause and effect relationships. This study offers new evidence regarding the effective mobilization of eWOM and narrative advertising in the context of Generation Z. The results indicate narrative advertising to have strong effects on Generation Z purchase intentions as compared to eWOM.

Sustainable Value Co-Creation in the Virtual Community: How Diversified Co-Creation Experience Affects Co-Creation Intention

The popularization of digital infrastructure has enabled the rise of the online game industry. Instead of targeting entertainment-oriented technology and services, which are the focus of most relevant studies, in the present study, we review the literature from the perspective of considering players of online games as both consumers of entertainment and co-creators of value. The three major antecedents of the theory of planned behavior, namely personal attitude toward co-creation, subjective norms and perceived behavioral control, were modified to explore the relevant constructs. Specifically, the diversity of co-creation experience was used to predict co-creation intention. The proposed model was empirically evaluated through the structural equation modeling of survey data collected from 321 World of Warcraft (WoW) players. As hypothesized, the diversified co-creation experience positively affected the antecedents. The findings provide implications on how to increase players’ participation in co-creation to achieve sustainable mutual benefits.

Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type

The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.

Using Bayesian Network to Predict Online Review Helpfulness

The enormous volume and largely varying quality of available reviews provide a great obstacle to seek out the most helpful reviews. While Naive Bayesian Network (NBN) is one of the matured artificial intelligence approaches for business decision support, the usage of NBN to predict the helpfulness of online reviews is lacking. This study intends to suggest HPNBN (a helpfulness prediction model using NBN), which adopts NBN for helpfulness prediction. This study crawled sample data from Amazon website and 8699 reviews comprise the final sample. Twenty-one predictors represent reviewer and textual traits as well as product traits of the reviews. We investigate how the expanded list of predictors including product, reviewer, and textual characteristics of eWOM (online word-of-mouth) has an effect on helpfulness by suggesting conditional probabilities of the binned determinants. The prediction accuracy of NBN outperformed that of the k-nearest neighbor (kNN) method and the neural network (NN) model. The results of this study can support determining helpfulness and support website design to induce review helpfulness. This study will help decision-makers predict the helpfulness of the review comments posted to their websites and manage more effective customer satisfaction strategies. When prospect customers feel such review helpfulness, they will have a stronger intention to pay a regular visit to the target website.