THE IMPACT OF ONLINE PRODUCT REVIEWS IN VIRTUAL COMMUNITY TOWARDS PURCHASE DECISIONS IN INDONESIAN DENIM INDUSTRY

In this study, the researcher uses a combination of the theory of planned behavior and the model of goal-directed behavior to map what effects online product reviews can have on consumer purchase decisions. The researcher hypothesizes that the two adapted theories contain factors that can positively influence consumer purchase decisions. The researcher uses PLS-SEM and open coding to analyze quantitative and qualitative data. The results showed that ten of the eighteen hypotheses tested were proven to be accepted. Consumers in the denim industry tend to perceive online product reviews as a key aspect of their decision-making process. The results of qualitative data analysis also showed similar results, all respondents indicated the importance of the influence of an online product review on their purchasing decisions. The researcher also discusses the implications of these results for both research and practice.

Online Product Review Analysis to Automate the Extraction of Customer Requirements

The increasing use of online retail platforms has generated an enormous amount of textual data on the user experiences with these products in online reviews. These reviews provide a rich resource to elicit customer requirements for a category of products. The recent research has explored this possibility to some extent. The study reported here investigates the coding of publically available user reviews to understand customer sentiments on environmentally-friendly products. The manual review typically consists of a qualitative analysis of textual content, which is a resource-intensive process. An automated procedure based on Aspect-Based Sentiment Analysis (ABSA) is proposed and explored in this study. This procedure can be beneficial in analyzing reviews of products that belong to a specific category. As a case study, environmentally-friendly products are used. Manual content analysis and automated ABSA-based analysis are performed on the same review data to extract customer sentiments. The results show that we obtain over a 50% classification accuracy for a multiclass classification NLP task with a very elementary word vector-based model. The drop in accuracy (compared to human annotation) can be offset because an automated system is thousands of times faster than a human. Given enough data, it will perform better than its human counterpart in tasks on customer requirement modeling. We also discuss the future routes that can be taken to extend our system by leveraging more sophisticated paradigms and substantially improving our system’s performance.

IMPACT ANALYSIS OF DIGITAL MARKETING: AN INDIAN PERSPECTIVE

The increasing use of digital media by consumers, companies utilizes digital marketing to outreach their market segments. The purpose of this study is to determine marketing strategies commonly utilized in digital communication and identify the preferred by consumers which influences decision making. Consumers have been identified as a driving force for online shopping. While there have been numerous studies about digital advertising, there has been little academic research focused on type of digital marketing strategies are preferred and influences their consumer’s behavior. A survey of 225 consumes indicated a preference for side panel ads and email ads; they do not like pop – up advertising. If provided a personal benefit like discount or reward they will write an online product review.

An alternative view of the millennial green product purchase: the roles of online product review and self-image congruence

PurposeThe paper aims to provide an alternative view to green consumption behaviors of millennials. In fact, studies on green consumption have usually assumed a common attitude–behavior model for different generations. Instead, the view in this paper highlights two other constructs, online product review and self-image congruence, as the key antecedents to the behavior among the generation.Design/methodology/approachTo test our proposed model, an online survey with a sample of 305 millennials in Vietnam was conducted. The sample shares similar demographic features with the millennials in the country. The data were collected in popular social networks and then validated before being analyzed with AMOS.FindingsThe model analysis results provided supports for the key roles of online product review and self-image congruence among millennials. In particular, online product review was found to have both direct and mediational impacts on green product purchase intention. Self-image congruence was also found to be a key antecedent to the intention.Research limitations/implicationsThe model in this paper only examined the purchase intention. Moreover, only a single sample of millennials in Vietnam was investigated. Future research may incorporate the green consumption behavior to enhance the external validity and/or directly compare models for different generations or across countries to further confirm the differential generational patterns.Practical implicationsThe paper includes recommendations for managers to use the online channels and to promote green product self-matching among millennials. These recommendations are not contrary to but go beyond the frequently suggested ones for attitude-related training or communication campaigns for green consumption.Originality/valueThis paper fills an identified gap to provide an alternative view to green consumption behaviors of millennials. Different from the common attitude–behavior view in green consumption research, two key constructs of online product review and self-image congruence are highlighted for the generation in this paper.

Sentiment Analysis of Online Product Reviews Using DLMNN and Future Prediction of Online Product Using IANFIS

A major task that the NLP (Natural Language Processing) has to follow is Sentiments analysis (SA) or opinions mining (OM). For finding whether the user's attitude is positive, neutral or negative, it captures each user's opinion, belief, and feelings about the corresponding product. Through this, needed changes can well be done on the product for better customer contentment by the companies. Most of the existing techniques on SA for these online products encompass very low accuracy and also consumed more time during training. By utilizing a Deep learning modified neural network (DLMNN), a method is proposed for SA of online product review and by means of Improved Adaptive Neuro-Fuzzy Inference System (IANFIS), a method is proposed for future prediction of online products to trounce the above-stated issues. Initially, the data values are partitioned into Grade-based (GB), Content-based (CB), and Collaboration based (CLB) scenarios from the dataset. After that, each scenario goes through review analysis (RA) by utilizing DLMNN, which brings about the results as positive, negative, as well as neutral reviews. IANFIS performs a weighting factor and classification on the product for future prediction. In the experimental evaluation, the proposed system gave a better performance compared to the existing methods.

Sentiment Analysis of Online Product Reviews Using DLMNN and Future Prediction of Online Product Using IANFIS

Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). It captures the user’s opinion, feelings, and belief regarding the respective product especially to determine whether the user’s attitude is positive, negative, or neutral. This analysis greatly helps the companies to make necessary changes in their product which in return can overcome the flaws that the product is facing and targets better customer satisfaction. Existing techniques for the sentiment analysis of online product reviews obtained low accuracy and also took more time for training. To overcome such issues in this paper, a DLMNN is proposed for sentiment analysis of online product review and IANFIS is proposed for future prediction of online product. Here, the sentiment analysis and future predictions are done on the products taken from the food review dataset. First, from the dataset, the data values are partitioned into GB, CB, and CLB scenarios and then the review analysis for each scenario is performed separately using DLMNN and they give the result as positive, negative, and neutral reviews for the product. After the process of review classification based on these three scenarios, the future prediction of the products is done by performing weighting factor and classification using IANFIS. Experimental results are compared with some existing techniques and the results show that the proposed method outperforms other existing algorithms.