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This research was conducted to analyze and know the influence of Online Customer Reviews, Influencer Marketing, Website Quality, on Online Purchasing Decisions at Shopee's Online Marketplace. The analytical method used in this research is descriptive analysis and multiple linear regression analysis. This type of research is associative research and the data used are primary data obtained with a list of questions whose measurements use an interval scale. Hypothesis testing is done using SPSS. The results of the research conducted indicate that simultaneously the variables Online Customer Review, Influencer Marketing, Quality Website have a significant effect on Purchasing Decisions Online on the Online Marketplace Shopee. However, partially it is known that in the results of the above research it is known that the coefficient value of the Online Customer Review variable 0.196 is negative with T count (1.742).

Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, especially the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. Academic/Practical Relevance: To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller's activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology: We collaborated with one of the largest online solar marketplaces in the U.S. that connects potential solar panel adopters with installers. We obtained a unique dataset from the marketplace for 2013 - 2018. We complement this with public datasets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. Results: We find that the dispersion in customer reviews has a significant and inverted U-shaped effect on an installer's marketplace activity level. Specifically, the installer's activity level increases with the review dispersion if and only if that dispersion is below a certain threshold. Above that threshold, more dispersion in reviews lowers the installer's activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial Implications: Our findings provide valuable insights to marketplace operators about the implications of review dispersions, and call for innovative platform design.

Education technologies (EdTech) are one of the fastest growing fields of contemporary teaching and main source of innovation that can enable revolutionary change in educational practice. Online customer reviews play a vital role in choosing among the various online platforms. Parents and teachers make use of these reviews to match with their preference. Similarly, the EdTech companies uses this information to improve their customer service. Importing the customer reviews from online websites and sentiment analysis on the reviews by customers as well as companies could be used for decision making to improve the service delivered by EdTech companies. Sentiment analysis is a supervised learning to identify positive, negative and neutral opinions from text which could be used to understand the overall sentiment of customers towards education technologies.

How much does a single negative customer review impact online search and purchase behavior? To answer this question, the authors compare choices made by consumers searching for a product that had a negative review posted on its product-page with those made when the same review had “moved” to a second page as additional reviews were posted. By focusing on where the review appears rather than whether it was submitted, our approach tackles concerns of spurious correlation between customer reviews and unobserved demand shocks. Using data from a large online retailer, we find that the discovery of a negative review – one that has 3-stars or less out of 5 – leads to an average drop in purchase probability of 51.4%, an increase in continuing search for a competing item of 11.4%, and a 15.8% increase in the price paid if a purchase occurs. Our findings are presented on a two-dimensional product map – an innovative way to evaluate the vulnerability of products to a single negative review.

Humans have entered the age of algorithms. Each minute, algorithms shape countless preferences from suggesting a product to a potential life partner. In the marketplace, algorithms are trained to learn consumer preferences from customer reviews because user-generated reviews are considered the voice of customers and a valuable source of information to firms. Insights, minded from reviews, play an indispensable role in several business activities ranging from product recommen-dations, targeted advertising, promotions, segmentation, etc. In this research, we question whether reviews might hold stereotypic gender bias that algorithms learn and propagate. Utilizing data from millions of observations and a word embedding approach, GloVe, we show that algorithms designed to learn from human language output, also learn gender bias. We also examine why such biases occur: whether the bias is caused because of a negative bias against females or a positive bias for males. We examine the impact of gender bias in reviews on choice and conclude with policy implications for female consumers, especially when they are unaware of the bias, and the ethical implications for the firms.

The disruption of sharing economy services to incumbent firms has attracted growing attention. Yet, the literature is silent on how incumbents respond to the rivalry and the resulted consequences. We investigate incumbent hotels’ adjustment on quality after home sharing’s entry using management responses, an online reputation marketing strategy to address feedback in customer reviews. Our method integrates quasi-experiments and machine learning to not only estimate hotels’ response to home sharing’s entry but also unveil the mechanism. We provide evidence on distinct responses to home sharing’s entry across different hotel price segments, which lead to divergent performance outcomes in terms of customer satisfaction and sales. Hotels that respond more actively to customer reviews demonstrate improved quality in service areas where home sharing typically leads - including the check-in/out process, cleanliness, excursion opportunity, and room condition -and receive higher sales. In contrast, hotels that respond less appear to lose to not only home sharing but also peer hotels that respond more to reviews. We provide implications on how incumbents should react to technological and business model disruptions.

Approval Ratings or customer reviews are just weighted/simple mean of all feedback scores given by a set of customers. These scores are dynamic and change as and when new feedback data is fed in. There is no way to predict the final score after a particular number of feedbacks would be achieved. Also there is no way to know the instantaneous variablitity in the score. Comparing and ranking products as per customer reviews is absurd since the number of feedbacks may not be same for these products. The below article is a mathematical way to sort out all these issues. It helps us predict the final score and also gives us instantaneous variability at any point.

With the increased use of the internet,the online purchasing has also become very common among the different classes of our society. One of the major reasons for the growth of this online purchasing trend in the people is the availability of the wide range of options to choose from. Other reasons include direct dealing with the manufacturers thereby resulting in cheaper products, exciting offers to the customers like cashback ,availing coupons,etc., 24 hour services, easy payment and delivery options. These websites or merchants also offer the facility to compare different products according to their features which help the customers in making decision according to their requirements. As a result of this,people give their feedback or express their thoughts about the product or the services received (Sreyasi Rupa De & Prof. Samir Kumar Bandyopadhyay, 2017). In this paper, we have made a comparative study of the different techniques used for analyzing the comments of the customers using Sentiment Analysis along with their advantages and drawbacks which will be beneficial in developing and improving the system.

Using a novel dataset of customer reviews from Amazon.com, I study the impact of managerial myopia on product market reputation. Using exogenous variation due to the timing of CEO equity vesting events, I show that short-term incentive shocks predict declines in reputation. A changing product market lineup and a deterioration of existing products are two mechanisms through which reputation is affected. The effect is larger when the CEO has other short-term concerns and when the firm has a low reputation in the product market. However, higher advertising expenses mitigate the negative reputational effect among consumers. Using an alternative empirical methodology, I find that higher short-term ownership in the firm is also associated with declining product market reputation, while higher long-term ownership is associated with increasing reputation.

The paper investigates the underlying relationship between service ratings and customer types based on hotel review data collected from Tripadvisor.com. Comprehensive statistical analysis has been conducted for the overall and six attribute ratings. The results suggest that there exist significant differences on the mean rating among different customer groups. In particular, Business customers are the most conservative while Couple customers are the most optimistic on average ratings. Further, the paper examines how each customer group weights different attributes towards the overall rating via regression analysis. For all groups, Service, Room and Value account for over 70% of the total weight, while distinct priorities exist for each individual group. Surprisingly, Hotel Star has insignificant impact for all groups of customers. Additional managerial insights have been developed between independent vs chain hotels and 2-3 star vs 4-5 star hotels. The study highlights the importance of customized service design among different customer groups.

On December 14, 2016, President Obama signed the Consumer Review Fairness Act of 2016 (CRFA or Act) into law. This first-of its kind federal law voids contract provisions that seek to prohibit, restrict or penalize online customer reviews. While the CRFA provides valuable and needed protections for online consumer expression, small businesses were still left without new options, outside of costly litigation, to protect themselves from fake negative reviews. This paper will provide an overview of the key provisions of the CRFA, including, what communications are covered, what protections are in place for review sites seeking to effectively police third party postings, and the permissible public and private causes of actions under the new law. This article will address how the Act does little to aid small businesses and recommends self-help remedies, outside of litigation, that should be available to provide assistance to small businesses victimized by social media trolls and bogus negative reviews.

Inspired by the literature on website visual design and feelings-as-information theory, the present research demonstrates that font may affect the impact of online reviews without changing review content. Specifically, two experiments show that the effect of review valence (positive vs. negative) on consumers’ product/service evaluation is stronger when the reviews are written in a difficult-to-read (vs. easy-to-read) font. However, this effect of font diminishes among high-involvement consumers. This research suggests that the processing ease of the reviews, which could be manipulated through the visual elements of online reviews may affects processing depth and thus the impact of the reviews.

First experiences are highly influential. Here, the authors show that nonfirst experiences can be made to seem like firsts and, consequently, to have a disproportionate influence on judgment. In six experiments, one piece of a series of information was framed to appear to have “first” status: For example, a weather report that appeared at the end of a sequence of weather reports happened to correspond to the first day of a vacation, and a customer review that appeared at the end of a sequence of hotel reviews happened to be the new year's first review. Such information had greater influence on subsequent judgments (e.g., of the next day's weather, of the hotel's quality) than identical information not framed as a first. This effect seems to arise largely because “phantom first” pieces of information receive greater weight, but not necessarily more attention, than other pieces of information.

Managerial responses to online customer reviews not only affect customers who receive the responses but also influence the behavior of subsequent customers who observe the responses. This externality arises because of the public nature of managerial responses. However, prior studies on managerial responses have been mainly limited to the offline setting where such externality rarely arises. In this exploratory study, we propose possible causes of the externality and empirically assess its magnitude. We leverage a natural experiment provided by two online travel agencies. Both agents allow customers to post reviews on hotels, but only one of the travel agents allows hotel management to post managerial responses. Using a difference-in-difference approach, we find that managerial responses have a significant and positive impact on the valence and volume of subsequent customer reviews. Average review rating increases by 15% and average review volume increases by 48% after the provision of managerial responses. We also find evidence that the increase in review volume can be partially attributed to increases in hotel sales. Our result highlights the importance of managing and responding to online customer reviews.

This paper analyzes the impact of online customer reviews on purchasing decisions and the influence of online customer or peer reviews compared to other channels of information such as offline press (expert reviews) and trial versions (personal reviews). Using 7,024 answers of an anonymous online survey administrated from June 18 to July 5, 2004, on one of the leading websites dedicated to video games in France, we show that i. online peer reviews gathered by consumers before purchasing video games positively influence purchasing decisions; ii. the effect of online peer reviews is as important as the effects of personal and expert reviews; iii. online customer reviews complement trial versions and expert reviews from magazines.