THE EFFECTS OF SOCIAL MEDIA ADVERTISING AMONG ECO-TOURISTS IN MALAYSIA: AN EMPIRICAL STUDY ON MALAYSIAN ECOTOURISM

The focus of the study is to achieve the maximum advertising value on ecotourists using social media. In this area, the main objective of the “Technology Acceptance Model (TAM)” is to increase the present awareness of social media marketing. This topic is primarily concerned with exploring the context of social media advertisement with knowledge, service, and its behavioural intent to use social media ads within the unique environment of ecotourism in Malaysia. The approach of the study is to analyse a survey of 395 local Malaysian tourists in Klang Valley to test the “Technology Acceptance Model (TAM)”. The empirical findings denote that social media advertisement in ecotourism has positive effects on “Perceived ease of use” and “Perceived usefulness,” in terms of its ‘Informativeness’ and “Service Functionality.” That, in effect, contributes to the behavioural purpose of using social media ecotourism advertisements. The discipline’s findings indicate that advertising in social media in ecotourism needs to be utilized to provide a quick understanding. Also, the advertising is updated continuously to ensure reliable and appropriate sources to meet ecotourists’ information requirements and the support of tourist product ratings. These traits should satisfy travellers, making them likely to re-visit different ecotourism sights. These appearances should satisfy tourists and allow them to re-visit various ecotourist attractions.

Are Minority Opinions Shared Less?

Product evaluation portals on the web that collect product ratings provide an excellent opportunity to observe opinion sharing in a natural setting. Evidence across different paradigms shows that minority opinions are shared less than majority opinions. This article reports a study testing whether this effect holds on product evaluation portals. We tracked the ratings of N = 76 products at 12 measurement points. We predicted that the higher (lower) the mean initial rating of a product, the more positive (negative) the newly contributed ratings will differ from this baseline – as an indication of the preferred sharing of majority compared to minority opinions. We found, however, that newly added ratings were on average less extreme than earlier ratings. These results can either be interpreted as regression to the mean or evidence for the preferred sharing of minority opinions.

The impact of COVID-19 on online product reviews

Purpose The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors have caused the psychological well-being of customers to deteriorate. By taking up the role of online reviews in the regulation of consumers’ moods, this study aims to examine the changes that have occurred in online product ratings, as well as the negative tone and word counts of product reviews during the COVID-19 pandemic. Design/methodology/approach This study examines the online reviews of 321 products in the pre-COVID, immediate COVID and extended COVID periods. This paper compares the changes that have taken place in product evaluations via various analysis of variance analyses. The authors also test the effect of COVID-related deaths on product evaluations via regression analyses. Findings The results indicate that online product ratings decreased sharply just after the outbreak of COVID-19. The study also found that the tone of reviews was found to be more negative and the length of reviews appeared to be longer in comparison to the pre-COVID-19 period. The results also revealed that the product type (experience vs search) moderated the effect of the pandemic in online reviews and the impact of COVID-19 on online product reviews diminished in the later stages of the ongoing pandemic. Practical implications Managers should be aware of the detrimental impact of pandemics on online product reviews and be more responsive to customer problems during the early stages of pandemics. Originality/value To the best of the authors’ knowledge, this is the first study that analyzes the effects of a pandemic on online product ratings and review content. As such, this study offers a timely contribution to the marketing literature.

Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

Review Pollution: Pedagogy for a Post-Truth Society

Consumer reviews on platforms like Amazon are summarized into star ratings, used to weight search results, and consulted by consumers to guide purchase decisions. They are emblematic of the interactive digital environment that has purportedly transferred power from marketers to ‘regular people,’ and yet they represent the infiltration of promotional concerns into online information, as has occurred in search and social media content. Consumers’ ratings and reviews do promotional work for brands—not just for products but the platforms that host reviews—that money can’t always buy. Gains in power by consumers are quickly met with new strategies of control by companies who depend on reviews for reputational capital. Focusing on ecommerce giant Amazon, this article examines the complexities of online reviews, where individual efforts to provide product feedback and help others make choices become transformed into an information commodity and promotional vehicle. It acknowledges the ambiguous nature of reviews due to the rise of industries and business practices that influence or fake reviews as a promotional strategy. In response are yet other business practices and platform policies aiming to provide better information to consumers, protect the image of platforms that host reviews, and punish ‘bad actors’ in competitive markets. The complexity in the production, regulation, and manipulation of product ratings and reviews illustrates how the high stakes of attention in digital spaces create fertile ground for disinformation, which only emphasizes to users that they inhabit a ‘post-truth’ reality online.

Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis

PurposeThe evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.Design/methodology/approachThis study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.FindingsThe results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.Originality/valueThis study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.

Does the length of a review matter in perceived helpfulness? The moderating role of product experience

PurposeThis paper aims to offer a framework explaining how product experience (i.e. think vs feel) and product involvement (high vs low) influence the helpfulness of online reviews. It also reexamined how online consumer review dimensions help to build online review helpfulness under different contexts.Design/methodology/approachData were collected using content analysis on 1,200 online customer reviews on 12 products from four categories to measure the relationships between online review dimensions and the helpfulness of reviews. The regression analysis and analysis of variance (ANOVA) were used to test the hypotheses.FindingsThe findings indicate that the effectiveness of length of a review is moderated by product type; for think products, longer reviews yield higher helpfulness. Furthermore, the level of consistency between individual review ratings and overall product ratings is associated with review helpfulness. The length of product descriptions and product ratings is moderated by the level of involvement. For products with high involvement, longer descriptions yield higher helpfulness.Originality/valueA conceptual connection to customer interaction is proposed by online customer reviews that vary by product type. The findings provide implications for online retailers to better manage online customer reviews and increase the value of product ratings.

Differential effects of online signals on sales performance of local brand clothing products

PurposeBusinesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.Design/methodology/approachThe multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).FindingsThe findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.Originality/valueThe study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

Prediction of E-Commerce Product Ratings Based on Similar Users

Together with the fast advancement of continuous expansion and the Internet of E-commerce scope, product quantity, as well as assortment, boost fast. Merchants offer many goods via going shopping customers and websites generally consider a huge amount of moment to discover the products of theirs.Within e-commerce sites, the item rating is among the primary key ingredients of an excellent pc user expertise. Many methods are working with whose users to consider the goods they wish. A comparable item suggestion is among the favorite modes working with whose customers look for items in line with the item scores. In general, the suggestions aren't personalized to a particular pc user. Exploring a great deal of solutions tends to make customers runoff as a result of the info clog but not offering proper reviews for solutions.Traditional algorithms has data sparsity and cold start issues. To overcome these problems we use cosine similarity method to identify the similarity between those vectors. The nearest similar vector ratings will be used during the estimation of the unknown ratings.The proposed methodology records ratings of each product from users and those are represented by a vector, and the cosine similarity is used a measure to identify the similarity between those vectors. The nearest similar vector ratings will be used during the estimation of the unknown ratings.Hence, By using the above approach it can overcome the above problems and also it can achieve high efficiency and accuracy in a simple manner.

EXPRESS: Interacting User-Generated Content Technologies: How Questions & Answers Affect Consumer Reviews

This article studies the question and answer (Q&A) technology of electronic commerce platforms, an increasingly common form of user-generated content that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, the authors show that Q&As complement consumer reviews: unlike reviews, questions are primarily asked pre-purchase and focus on clarification of product attributes rather than discussion of quality; answers convey fit-specific information in a predominantly sentiment-free way. Based on these observations, the authors hypothesize that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. Indeed, when products suffering from fit mismatch start receiving Q&As, their subsequent ratings improve by approximately 0.1 to 0.5 stars and the fraction of negative reviews that discuss fit-related issues declines. The extent of the rating increase due to Q&As is proportional to the probability that purchasers will experience fit mismatch without Q&A. These findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings of products that have incurred low ratings due to customer-product fit mismatch.