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This study aims to examine the effects of three green information quality dimensions – persuasiveness, completeness and credibility – on green brand evaluation and whether this is mediated by green brand credibility. It also examines the moderating effects of eco-label credibility and consumer knowledge on green information quality dimensions and green brand credibility relationships.

Using a structured questionnaire on environmentally-friendly electrical goods/electronics, cosmetic and apparel product advertisements, involving an elaboration task, this study collected usable data from 1,282 Indian consumers across 50 cities. It also undertook an assessment for three different product groups using structural equation modelling to examine proposed hypotheses and assessed moderated mediation using the Hays process model.

The study indicates that: green brand credibility mediates the effects of green information quality dimensions on green brand evaluation; consumer knowledge moderates the effects of persuasiveness and completeness on green brand credibility and eco-label credibility moderates the effects of persuasiveness and credibility on green brand credibility.

In green information processing, this study supports the relevance of the elaboration likelihood model and the mediation effect of green brand credibility. It also presents evidence that credible eco-labels enhance green information processing. While the results are broadly consistent across the three product categories, the results may only generalizable to the environmentally-aware urban populations.

Help brand managers to design advertisements that add brand credibility in environmentally-aware urban markets.

It helps to define green information quality and the interacting effects of eco-label credibility and consumer knowledge in green information processing.

The online Question and Answer community is full of a large number of science and technology topics, the discussion and dissemination of which play an important role in promoting the popularization of new technologies and cultivating public enthusiasm for science. However, the spread of false information and rumors weakens the community's positive effect, making the community more difficult for people to obtain useful information on such topics. Research on the influencing factors and governance of the spread of false information on science and technology topics has become the key to the spread of popular science.

Therefore, this paper uses the Elaboration Likelihood Model as the theoretical framework to examine the role of the factors influencing the spread of false information on science and technology topics in Zhihu community on the information persuasion and the impact on public behavior attitude from the core path and the edge path. This paper compiles a crawler program to capture 12,893 response information under the “Metaverse” topic in Zhihu community as an empirical sample and uses text mining and conducts visual correlation analysis to explore the key factors affecting the persuasive transmission path of information on science and technology topics.

The research finds that the content specialization, content consistency and content coherence of science and technology topics affect personal judgment from the aspect of information content through the core path and have a positive correlation with information persuasion; the number of comments, the length of the text and the publishing authors' influence from the edge image characteristics through the edge path are positively correlated with the information persuasion. Then, from the perspective of topic platform, government and topic participants, this paper puts forward a general plan to improve the information persuasion of science and technology topics so as to deal with false information.

Compared with the small data set of the traditional questionnaire survey, the research based on community empirical big data is more reliable. The model takes into account the attitude and behavior of users and is more suitable for the research on the transmission path of scientific and technological information in the internet era. This research provides a direction for analyzing the text characteristics and development trends of information in the field of science and technology and is conducive to promoting the optimization of the network information environment and building a good ecology, with the spread of rumors about science and technology topics curbed and the governance of false information strengthened.

The purpose of this paper is to identify the key determinants of the information value of enterprises’ micro-blogs, drawing on the consumer’s perspective. This paper examines the effects of such information value on consumers’ purchase intention through the building of online brand equity. The paper also investigates how perceived usefulness intervenes in the relationship between micro-blog information value and consumer purchase intention.

The proposed research model is examined empirically using a survey of 315 users of the Sina micro-blog. The statistical analysis applies a method based on variance using partial least squares.

The results show that online retailers’ micro-blog posts, which are characterized by informativeness and persuasiveness as perceived by customers, positively impact online retailers’ brand equity, which in turn influences consumer purchase intention. In addition, perceived information usefulness is found to be an important contextual factor that moderates the relationship between perceived persuasiveness and online retailer brand equity.

This work adds significance to the literature by extending the research on micro-blog marketing and also provides practical recommendations for online retailers on the operation of micro-blogs.

This paper aims to investigate the effect of product-related description abstractness/concreteness on perceived trustworthiness and the role of consumer product expertise and shopping-stage mindset in the persuasiveness of abstract vs concrete product descriptions.

Two online experiments were conducted: Study 1 (description abstractness – manipulated between-subject; consumer product expertise, perceived trustworthiness, purchase intent – measured), Study 2 (consumer shopping-stage mindset – manipulated between-subject; description abstractness – manipulated within-subject; consumer product expertise, perceived trustworthiness, abstract/concrete description preference – measured).

The negative effect of the abstractness (abstract descriptions vs the ones supplemented with relevant product details) on description trustworthiness was evidenced in Study 1. Trustworthiness was positively related to purchase intent, especially for high product expertise. Study 2 replicated the effect of product description abstractness on its trustworthiness in terms of two other forms of abstractness (abstract descriptions vs the ones supplemented with irrelevant product details and product benefits vs attributes). The goal-oriented (vs comparative) mindset had a positive effect on the benefit (vs attribute) description preference, especially for high product expertise.

For marketers, the results suggest the positive consequences of presenting concrete information on product attributes and the conditions enhancing the effectiveness of presenting product benefits.

The paper integrates the existing views on consumer response to abstract vs concrete information (lexical abstractness/concreteness, means-end chain theory) and links them to consumer product expertise and shopping-stage mindset.

The purpose of this study is to explore the effects of review group’s content-related and environment-associated attributes on information adoption intention of information readers. In addition, this study further investigates the effects of these determinants in different websites with different commercial attributes (i.e. online stores and third party forums).

This study used a structured online survey to collect data from an online store and a third party forum in China; totally 302 responses were collected.

The empirical results confirm that all of the five determinants significantly affect the information adoption intention of information readers. Furthermore, the authors found that four of the determinants have distinct effects in different websites.

The findings of this study validate the significant moderating role of website attributes in readers’ information processing. Information readers use distinct criteria to evaluate the received review information; electronic word-of-mouth determinants have varying effects on different websites with different commercial attributes.

This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.

Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.

Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.

The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.

The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.

The purpose of this paper is to examine the synergistic effects of online multimedia by categorizing it into online broadcast media (OBM) and online interactive media (OIM).

The authors used an online experiment method to manipulate the online message stimuli level (online media synergy and online single media repetition).

The results revealed that participants exposed to message stimuli of online media synergy reported greater source credibility, cognitive responses (brand credibility and positive thoughts about the brand), attitude toward the brand and purchase intention. In online multimedia, source credibility influences attitude toward the brand through brand credibility and positive thoughts about the brand; in online single media repetition, source credibility influences attitude toward the brand through only brand credibility.

In addition, the relationship between online media synergy and marketing outcomes might be moderated by consumers’ goals and thought patterns, and future research could further explore the moderating effects of these variables.

This study contributes to media synergy research, assists marketing planners in their understanding of the importance of online media synergy and serves as a reference for marketing planners considering an integrated online marketing plan.

The current study investigated how the synergy of OBM and OIM influences message persuasiveness for consumers (cognitive responses, attitude toward the brand and purchase intention).

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

Although microblogs have become an important information source, the credibility of their postings is still a critical concern due to the open and unregulated nature. To understand the antecedents of microblog information credibility, the purpose of this paper is to investigate the dual-role of cognitive heuristics (i.e. the additivity and bias roles) and the effect of gender differences.

This study collected data via an online field survey of active microblog users, and a total of 204 valid responses was received.

This study demonstrates the dual-role of source credibility and vividness, the additivity role of microblog platform credibility, and the bias role of social endorsement. Furthermore, this study also found out gender difference that the additivity role of cognitive heuristics was stronger for men while bias role was stronger for women.

This research enriches the microblog literature by examining the cognitive heuristic determinants as key predictors of microblog information credibility, and contributes to the information credibility literature by identifying and analyzing the dual-role effect of cognitive heuristics and corresponding gender differences.

This study can help organizations better manage their reputation, especially during the reputation crises, and also serves as a reminder to microblog platform operators of the importance of their microblog platform credibility.

This study can help organizations better manage their reputation, especially during the reputation crises, and serves as a reminder to the microblog platform operators of the importance of their microblog platform credibility.

This study investigates the dual-role effect of cognitive heuristics (i.e. the additivity role and bias role) and corresponding gender differences that are less touched on before, and thus provides a more nuanced understanding of the more complex effects of cognitive heuristics.

The purpose of this paper is to suggest important determinants for helpfulness from the reviews’ product data, review characteristics, and textual characteristics, and identify the more crucial factors among these determinants by using statistical methods. Furthermore, this study intends to propose a classification-based review recommender using a decision tree (CRDT) that uses a decision tree to identify and recommend reviews that have a high level of helpfulness.

This study used publicly available data from Amazon.com to construct measures of determinants and helpfulness. To examine this, the authors collected data about economic transactions on Amazon.com and analyzed the associated review system. The final sample included 10,000 reviews composed of 4,799 helpful and 5,201 not helpful reviews.

The study selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics through using a t-test and logistics regression. The five important variables found to be significant in both t-test and logistic regression analysis were the total number of reviews for the product, the reviewer’s history macro, the reviewer’s rank, the disclosure of the reviewer’s name, and the length of the review in words. The decision tree method produced decision rules for determining helpfulness from the value of the product data, review characteristics, and textual characteristics. The prediction accuracy of CRDT was better than that of the k-nearest neighbor (kNN) method and linear multivariate discriminant analysis in terms of prediction error. CRDT can suggest better determinants that have a greater effect on the degree of helpfulness.

The important factors suggested as affecting review helpfulness should be considered in the design of websites, as online retail sites with more helpful reviews can provide a greater potential value to customers. The results of the study suggest managers and marketers better understand customers’ review and increase the value to customers by proving enhanced diagnosticity to consumers.

This study is different from previous studies in that it investigated the holistic aspect of determinants, that is, product, review, and textual characteristics for classifying helpful reviews, and selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics by using a t-test and logistics regression. This study utilized a decision tree, which has rarely been used in predicting review helpfulness, to provide rules for identifying helpful online reviews.